MODEL PREDICTIVE CONTROL OF A POLYMER EXCHANGE MEMBRANE FUEL CELL

Abstract

This research presents the application of model predictive control (MPC) to control a proton exchange membrane fuel cell (PEMFC). Firstly, the steady state analysis of PEMFC is considered to select its suitable operating conditions based on cell electrical characteristics. Then, the effect of input parameters on cell voltage and temperature is analyzed to investigate the dynamic behavior of PEMFC that is important for control design. It is found that the cell voltage and cell temperature depend on the inlet molar flow rates and temperature of hydrogen and air, and operating current density. To obtain an efficient control system, the control structure design of the PEMFC is considered to specify a good choice of the controlled and manipulated variables. An analysis of the steady-state relative gain array (RGA) is used for pairing of the controlled and manipulated variables. The result shows that the inlet molar flow rates of hydrogen and air are manipulated variables to regulate the cell temperature and partial pressure of hydrogen, respectively. Finally, a model predictive control (MPC) is developed and designed for controlling the cell temperature and partial pressure of hydrogen. Basically, MPC requires the process model used in its control algorithm. The PEMFC model is known to be complicated and involves uncertain parameters. Thus, an offline robust model predictive control (robust MPC) based on a linear time-varying (LTV) model is proposed for PEMFC control. The simulation results show that the robust MPC shows better control performance than conventional MPC because the robust MPC can guarantee robust stability.